11 papers · 1 filter
Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods
Jiali Cheng, Chirag Agarwal, Hadi Amiri
Knowledge distillation (KD) is an effective method for model compression and transferring knowledge between models. However, its effect on model's robustness against spurious corre…
WebATLAS: An LLM Agent with Experience-Driven Memory and Action Simulation
Jiali Cheng, Anjishnu Kumar, Roshan Lal +7
Large Language Model (LLM) web agents often struggle with long-horizon web navigation and web task completion in new websites, producing inefficient action sequences unless fine-tu…
FUTURE: Flexible Unlearning for Tree Ensemble
Ziheng Chen, Jin Huang, Jiali Cheng +5
Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finan…
Speech Unlearning
Jiali Cheng, Hadi Amiri
We introduce machine unlearning for speech tasks, a novel and underexplored research problem that aims to efficiently and effectively remove the influence of specific data from tra…
Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning
Kristin Qi, Jiali Cheng, Youxiang Zhu +2
Detecting Mild Cognitive Impairment from picture descriptions is critical yet challenging, especially in multilingual and multiple picture settings. Prior work has primarily focuse…
Understanding Machine Unlearning Through the Lens of Mode Connectivity
Jiali Cheng, Hadi Amiri
Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch. Despite recent progress, the loss landscape and optimization geome…
Linguistic Blind Spots of Large Language Models
Jiali Cheng, Hadi Amiri
Large language models (LLMs) are the foundation of many AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding…
EqualizeIR: Mitigating Linguistic Biases in Retrieval Models
Jiali Cheng, Hadi Amiri
This study finds that existing information retrieval (IR) models show significant biases based on the linguistic complexity of input queries, performing well on linguistically simp…
FairFlow: Mitigating Dataset Biases through Undecided Learning
Jiali Cheng, Hadi Amiri
Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing fr…
FROG: Fair Removal on Graphs
Ziheng Chen, Jiali Cheng, Hadi Amiri +5
With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many o…
Tool Unlearning for Tool-Augmented LLMs
Jiali Cheng, Hadi Amiri
Tool-augmented large language models (LLMs) are often trained on datasets of query-response pairs, which embed the ability to use tools or APIs directly into the parametric knowled…